A name given to the log-odds function, which maps probabilities to the real line.
1
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3answers
63 views
How to test for simultaneous equality of choosen coefficients in logit or probit model?
How to test for simultaneous equality of choosen coefficients in logit or probit model ? What is the standard approach and what is the state of art approach ?
6
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2answers
54 views
How to incorporate costs (into logit model) of false positive, false negative, true positive, true negative if they are different costs?
How to incorporate costs (into logit model) of false positive, false negative, true positive, true negative responses, if they are different costs ? Is it possible to do that on the level of ...
1
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0answers
28 views
Estimate multinomial probit model with mlogit (R package)
From the document and help, probit model is supported by mlogit. But when I tried it with these R scripts, the estimation takes much longer time to run (than the logit verion) and the result is quite ...
0
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0answers
6 views
Definition of the unobserved term in a discrete choice logit with no attributes of the alternatives
In a discrete choice analysis, with a logit model with attributes of the the person but no attributes of the alternatives, the unobserved/stochastic term is assumed to have a logistic distribution. ...
1
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1answer
28 views
Finding Degree of Freedom from 2 Logit Models
Hi I have a question on finding DF in logit models. Say I have a table (all fictional numbers)
and say I am testing goodness of fit (deviance) between the fitted model and saturated model.
1) ...
0
votes
0answers
21 views
interpret Alleffects() from effects package [duplicate]
I have this example logit model where some of the variables are factors but I'm not too sure how to interpret the effects. If I understand logit models correctly the coefficients that we get from the ...
2
votes
1answer
86 views
Comparing two logit or probit curves using a single parameter
I've conducted a psychological experiment on the same subject, under two different condition. For each condition I've collected the number of correct and wrong answer for each stimulus (number of ...
4
votes
1answer
83 views
Dealing with 'Don't Know' answers for a categorical outcome variable
I have a survey data with categorical outcome variable (yes, no, don't know) which reflects the acceptance of some situation by respondents. My concern is how to deal with Don't know answers, I really ...
0
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0answers
34 views
Simple lmer model specification help needed
We would like to analyze some of our fixation probability data with the lme framework. This is binomial data collected over several hundred trials with several dozen subjects. We want to asses whether ...
0
votes
0answers
31 views
Variance of MLE correctly specified vs misspecified model
I am calculating the variance of an MLE, once assuming that the model is correctly specified, and $V0=(H^{-1})$
and then assuming it's incorrectly specified, so that $V1=(H^{-1} \Sigma H^{-1})^{-1}$
...
0
votes
1answer
83 views
Equation for a logit link function for a series of events
I have modeled some data using generalized linear modeling with a binomial distribution and logit link function. However, my data is not dichotomous, it is actually a series of events. I have fixed ...
0
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3answers
54 views
How to rebalance a dataset for a logit model in Stata?
I want to use logit model in Stata. In my dataset only 3% of my target variable observations are 1, and 97% are 0.
How do I rebalance this data set, so I have more observations labelled 1, and fewer ...
0
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1answer
46 views
Alternative to LPM and logit regression in Stata
I'm dealing currently with complex dataset that was already tested with Linear probability model and logit regression. I'd like to find an alternative for original regressions.
The sample uses a ...
3
votes
2answers
172 views
What does the logit value actually mean?
I have a logit model that comes up with a number between 0 and 1 for many cases, but how can we interprete this?
Lets take a case with a logit of 0.20
Can we assert that there is 20% probability ...
0
votes
0answers
13 views
mlogit specifying alt.avr in block design
I am trying to use mlogit in R to analyse a block design choice experiment dataset. The 12 choice sets/cards have been randomly assigned to 3 different blocks and ...
0
votes
1answer
85 views
What data cleaning to do for logit regression with only dummies?
Does anyone know what exact data cleaning steps one need to undertake in order to clean data for a logit regression (not a logistic regression)?
I have only time variables, meaning year and month, as ...
0
votes
0answers
56 views
Logistic regression state-space representation
Consider this univariate time series Logit model:
$\text{Pr}(X_{t}=1)=\frac{e^{\beta_{1}+\beta_{2}x_{t}+\epsilon_{t}}}{1+e^{\beta_{1}+\beta_{2}x_{t}+\epsilon_{t}}}$,
then
...
2
votes
0answers
43 views
Categorical logit Predictor with too many different levels
One of the predictors I had in a logit model is "City". Problem is this categorical variable has too many factor levels. e.g. In a Sample of ~3000 there are already ~200 different cities.
Is it ...
0
votes
1answer
99 views
Problem with interaction variable for logit regression
I have a short question regarding interaction variables:
In a logit regression with 2 independent dichotomous variables (A and B), both variables are significant. By including the interaction (AxB) ...
0
votes
0answers
103 views
probit tobit model
I am using a dataset with approx 2000 obs to look at the effect of a number of covariates on:
whether a person is unemployed or not (binary)
the period of time a person has been unemployed (Q ...
2
votes
3answers
86 views
What to do when the predictors do not accord with common sense/literature, but the model is fine/best according to log likelihood and LRT?
I would try to clarify the problem and then ask the questions.
The problem (variable names are masked due to confidentiality):
I ran a binary logistic regression, in which there were 5 ...
-1
votes
1answer
64 views
How to show what is going on if we drop significiant variable in logit model?
How to show what is going on if we drop significant variable in logit model ? Bias and heteroskedasticity should emerge. But what is the framework for showing such behaviours in econometric models ?
0
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1answer
51 views
Finding choice probabilities by using utility with logit and probit models
I am using a formula to calculate the utility, which is as follows:
v_{ij} = 1 - x*beta + delta_i + e_{ij}
delta_i ~ N(0,phi^2)
e_ij ~ N(0,sigma^2)
v_{ij} is ...
1
vote
0answers
85 views
Variance of log-odds probabilities
I am wondering how I could express the variance of log-odds into understandable terms.
For example the variance in the log-odds of crime being reported to the police between neighbourhoods is 0.07 ...
0
votes
1answer
55 views
Scale parameter
I came across the scale parameter used in the logit and probit models. Does any one know what that is and what it is used for? What would go wrong if I did not use it?
1
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1answer
123 views
Exploratory data analysis for discrete data
I am using a probit and a logit model for obtaining the choice probabilities of some data. What kind of plots can be useful to conduct a exploratory data analysis for these data?
Here is the ...
0
votes
0answers
71 views
Ordered Probit/Logit with random coefficients
I searched everywhere but I didn't find what I want, that is why I as the question here. Does anybody know of a function in R which allows to estimate ordered ...
5
votes
4answers
321 views
Beta regression of proportion data including 1 and 0
I am trying to produce a model for which I have a response variable which is a proportion between 0 and 1, this includes quite a few 0s and 1s but also many values in between. I am thinking about ...
1
vote
0answers
48 views
Logit versus Probit [duplicate]
Possible Duplicate:
Difference between logit and probit models
I have data in which the response variable is binary. So, I fitted logit and probit models and obtained the results. How can I ...
1
vote
1answer
136 views
Meaning of intercept in multinomial regression with binary predictors?
I am doing a multinomial regression and trying to interpret the results: In the basic model there is only one binary predictor variable (0 = high risk scenario, 1 = low risk scenario), the dependent ...
0
votes
0answers
120 views
What are some software packages for doing panel aggregated logistic regression with no random or fixed effect intercept (not SAS)?
I am interested in doing Panel Aggregated Logistic Regression (aka Panel Binomial Logistic regression) without random intercept and I was wondering if anyone could suggest some existing packages.
...
1
vote
0answers
43 views
Replication in multivariate logit in R
I am new to both logit models and GLMs, but I think one of those two model classes might be the correct analysis for my data set:
I am interested in comparing the composition of the diet of fish ...
1
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0answers
67 views
Individual-level parameter estimates from ordered choice regressions
I have got a question regarding ordered choice regressions in R.
I have several demographic variables with which I want to explain the ordered choice of individuals within a survey in an ordered ...
0
votes
0answers
69 views
How to transform an independent variable with parabolic shaped logit plot in logistic regression?
I am building a logistic regression model. I have created some ratio variables. When I look at the logit plot (log odds) of these ratio variables, some of them follow parabolic shape. I am wondering ...
0
votes
0answers
21 views
Chamberlain fixed effect model in R [duplicate]
Possible Duplicate:
R package for fixed-effect logistic regression
Hey I want to ask if anyone knows some functions or libraries in R that applies the Chamberlain (1980) method in ...
0
votes
1answer
55 views
Adding not chosen alternatives as data to logistic regression model
I am interested in predicting shopping behaviour in a shopping center. I have a database with the chosen alternatives (shop) and variables describing that alternative (like type and size) and the ...
3
votes
1answer
539 views
Predicting ordered logit in R
Pardon my naïveté if this is a dumb question, but I'm new to R. I'm trying to do an ordered logit regression. I'm running the model like so (just a dumb little model estimating number of firms in a ...
1
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0answers
67 views
Is there a better alternative to a logit / probit regression when all dependent variables are dichotomous?
I'm working on a clinical trial dataset with binary response. All dependent variables are also binary. My first impulse was to simply run a standard logit/probit regression and be done with it. But ...
0
votes
0answers
413 views
Panel data: Logistic fixed-effect model with a non-varying / constant dependent variable
As a part of my master’s thesis, I am currently undertaking a statistical analysis of corporate turnaround. I have a panel dataset consisting of approximately 200 firms for 6 years each gathered ...
6
votes
1answer
504 views
Can I interpret the inclusion of a quadratic term in logistic regression as indicating a turning point?
In a Logistic Regression with linear and quadratic terms only, if I have a linear coefficient $\beta_1$ and quadratic coefficient $\beta_2$, can I say that that there is turning point of the ...
0
votes
0answers
21 views
which interactions to include? [duplicate]
Possible Duplicate:
What terms should I include in a linear regression model?
In case of running a logistic regression, with multiple variables which result in strongly disagree to strongly ...
6
votes
3answers
332 views
Which logit/probit model do I use for multiple reponse/dependent variables?
I have $300$ time series objects that constitute the $300$ columns of matrix $X$. This matrix has $5$ rows and represents $5$ days of time series information for each $300$ columns.
I set up a ...
3
votes
0answers
39 views
Combination of multinomial logit model and logit model
I want to analyze the determinants of credit constraints of a firm. I have information for both formal and informal credit. I have 6 categories of credit-constraint statuses of a firm for formal ...
1
vote
0answers
80 views
Report coefficients or odds ratio in ordinal logit/probit?
I'm doing ordinal logit/probit only to analyse the direction of causality (e.g. if some variable makes it more likely to observe a low scale or a high scale). No interpretation is needed beyond this.
...
3
votes
1answer
583 views
Negative coefficient in ordered logistic regression
Suppose we have the ordinal response $y:\{Bad,Neutral,Good\} \rightarrow \{1,2,3\}$ and a set of variables $X:=[x_1,x_2,x_3]$ that we think will explain $y$. We then do an ordered logistic regression ...
1
vote
1answer
191 views
Using multinomial regression's coefficients to derive predicted outcomes in C#
I am attempting to use C# (and the alglib library) to calculate the predicted probability that an outcome ends up in one of five classes. I have managed to calculate parameter estimates (i.e. slope ...
1
vote
1answer
165 views
Can a contingency table be used to model probabilities?
Its been a while since I did any serious statistics. I have been reading about contingency tables recently and it seems like they may offer a solution to my problem. There are people on here that know ...
0
votes
0answers
240 views
Implementing McKelvey & Zavoina R2 for drc package in R
I'm implementing the McKelvey & Zavoina pseudo-R2 measure for use with the drc package and intend to open-source the solution and contribute to that project.
I ...
8
votes
1answer
323 views
Choose best model between logit, probit and nls
I'm analyzing a certain dataset, and I need to understand how to choose the best model that fits my data. I'm using R.
An example of data I have is the following:
...
1
vote
0answers
369 views
Singularity issues in multinomial model using R
I am trying to develop a mode choice model (4 modes: hov, transit, bike, walk) and below are two approaches I am using. I am having problems in both
Approach 1
Mode choice as a function of price ...
